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ML based Prototype for Skin Cancer Detection 3

ML based Prototype for Skin Cancer Detection

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3rd international conference on Communication, computing and Industry 4.0, C2I4 2022

作者： Saju, Binju Asha, V. Murali, Sarath C Vinayaka, D. Kumar, Vikash Nithya, B. Master of Computer Applications New Horizon College of Engineering Bengaluru India

ISBN: (纸本)9781665474979

Skin malignant growth is one of the most lethal types of disease, and the demise rate has essentially become because of an absence of consciousness of the markers and protection measures. Therefore, in order to stop the spread of cancer, early identification at an early stage is essential. Skin cancer is further classified into several forms, with melanoma, nevus and seborrheickeratosis. This study utilizes Artificial Intelligence (AI) and picture-handling techniques to recognize and classify various types of skin cancer. Dermoscopic pictures are thought about as contributions during the pre-handling steps. The production of a Convolutional Neural Network based melanoma detection model. If melanoma is not found in its early stages, it can be fatal. It is responsible for 75% of skin cancer fatalities. A system that can analyse photos and notify dermatologists of the existence of melanoma might potentially eliminate the need for a lot of manual diagnosis work. Similar findings to those of the pertained model were produced by our created model. In simulations using the 2017 international Skin Imaging Collaboration skin cancer dataset, the suggested method performed admirably. The Convolutional neural network was able to achieve a 92 % accuracy rate. © 2022 IEEE.

关键词： Convolutional neural network deep learning ISIC skin lesion/cancer machine learning

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Optimizing Crime Prevention Strategies Using machine learning Models

Optimizing Crime Prevention Strategies Using Machine Learnin...

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Women in computing (InCoWoCo), international conference for

作者： V. Ajitha S. Irin Sherly E. Venitha M. Senthilkumar N. Bhavyadevi Vishalakshi R. Prathipa Department of CSE Nalla Malla Reddy Engineering College Hyderabad India Department of CSE SIMATS Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai India Department of CSE Rajalakshmi Institute of Technology Chennai India Department of AI & DS Kongunadu College of Engineering and Technology Thottiyam Trichy India Department of CSE SOET CMR University Bengaluru India Department of ECE Panimalar Engineering College Chennai India

ISBN: (数字)9798331518943

ISBN: (纸本)9798331518950

Crime rates are on the rise, necessitating effective strategies for prevention. machine learning has emerged as a promising tool for forecasting crime incidents due to its ability to learn from data autonomously. The research on the Utilization of machine learning Algorithms in Crime Forecasting investigates innovative approaches to anticipate and diminish crime rates. By utilizing information from the San Francisco Police Department, diverse machine learning algorithms are employed to scrutinize and predict crime patterns. The methodology includes data collection, preprocessing, and the implementation of predictive models like K-Nearest Neighbors (KNN), Support Vector machines (SVM), and Decision Trees. These models were trained on a preprocessed dataset of crime statistics from 2014 to 2017, and their performance was evaluated using accuracy and F-beta score metrics. Key results show that the Decision Tree algorithm achieved the highest accuracy at 88%, with KNN following closely at 87%. SVM, although effective in other scenarios, attained a lower accuracy of 66% in this study. These findings indicate that Decision Trees and KNN are particularly effective for classifying and predicting crime data.

关键词： Support vector machines Training Accuracy machine learning algorithms Nearest neighbor methods Predictive models Prediction algorithms Classification algorithms Decision trees Forecasting

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AIOps and Sustainability: Transforming Data Centers for a Greener Future

AIOps and Sustainability: Transforming Data Centers for a Gr...

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High Performance computing, Networking, Storage and Analysis, SC-W: Workshops of the international conference for

作者： Subrahmanya Vinayak Joshi Sergey Serebryakov Deepak Nanjundaiah Tejas Hegde HPC AI & Labs Hewlett Packard Enterprise

ISBN: (数字)9798350355543

ISBN: (纸本)9798350355550

Enterprise and high-performance computing (HPC) data centers are dealing with thousands of sensor metrics and associated data. A top-end target for exascale machines is 10 million data points per second. The escalating volume and speed of data generation are making management of such systems more difficult, and outages are increasing. The latest Uptime Institute’s Outage Analysis report [1], published in June 2022, states that 30% of all outages in 2021 lasted more than 24 hours, a disturbing increase from 8% in 2017. While equipment is idle during downtime, it often continues to consume power, especially for cooling systems. This leads to wasted energy and higher operational costs. We propose an AIOps solution that uses advanced data analytics, machine learning, and deep learning methods to develop automated and advanced anomaly detection and predictive tools for data centers. They perform at scale and speed, and improve data center resiliency and energy efficiency, thereby promoting the sustainability of data centers.

关键词： Measurement Deep learning Data centers High performance computing Predictive models Real-time systems Sustainable development Anomaly detection Resilience Predictive maintenance

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A Comparative Study of Time Series, machine learning, and Ensemble Models for Crude Oil Price Prediction 1

引用

international conference on Advanced computing and Intelligent Technologies, ICACIT 2022

作者： Prakash, Ankit Singh, Sunil Kumar Department of Computer Science and Information Technology Mahatma Gandhi Central University Bihar Motihari India

ISBN: (数字)9789811929809

ISBN: (纸本)9789811929793

Fuel costs are falling worldwide during the pandemic, but not in India. In recent years, the petrol’s price has risen dramatically in all across the states of India, and reaching in three digits. The government is also targeting it as their best revenue source. It indicates that the fuel costs causes more inflation and common people have to suffer. These increased prices have robbed Indian citizens of living a healthy life. This paper analyzes Delhi’s petrol price and forecasting by examining daily petrol prices’ forecasting performance from June 16, 2017, to December 15, 2021. Various time series, machine learning, deep learning, and ensemble learning models are used to find the best model for forecasting the results. We have analyzed the performance of the models with the help of the performance metrics such as MSE, RMSE, MAPE, and NRMSE. The outcomes of the models indicate that time series-based models are pretty compelling. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Time series

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Video QoE Inference with machine learning 17

Video QoE Inference with Machine Learning

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17th IEEE international Wireless Communications and Mobile computing conference (IEEE IWCMC)

作者： Tisa-Selma Bentaleb, Abdelhak Harous, Saad United Arab Emirates Univ Al Ain U Arab Emirates Natl Univ Singapore Singapore Singapore

ISBN: (纸本)9781728186160

HTTP adaptive streaming (HAS) has become the de-facto standard for delivering video over the Internet. More content providers like YouTube and Twitch have started generating and delivering high quality streams (usually 4k resolution) with advanced end-to-end encryption mechanisms. This huge increase in HAS encrypted traffic, creates a significant challenge for network providers in understanding what is happening on their infrastructures which limits their ability to manage network infrastructures properly. Due to such invisibility, the network providers could not take appropriate decisions for better optimizations, resulting in significant revenue lost. Inferring the quality of experience (QoE) of HAS-based streaming video services is important, but recent studies highlight that most of existing solutions that rely on packet inspections, showing low performance in inference accuracy. To address this issue, we develop a machine learning powered system that infers QoE factors such as startup delay, rebuffering and selected quality, for encrypted on-demand HAS streaming video services. Our solution uses two data-driven techniques: Deep Self Organizing Map (DSOM) and Multi Layer Perceptron Backpropagation (MLPB), allowing efficient accuracy with low error in inferring QoE factors over several public video datasets, compared to some state-of-the-art approaches.

关键词： QoE encrypted video traffic inferring QoE deep learning machine learning SOM MLPB

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Customizable DDoS Attack Data Generation in SDN Environments for Enhanced machine learning Detection Models

Customizable DDoS Attack Data Generation in SDN Environments...

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Advanced Research in computing (ICARC), international conference on

作者： Nadeera Gayantha Chathura Rajapakse Janaka Senanayake Dept. of Industrial Management University of Kelaniya Sri Lanka School of Computing Engineering and Technology Robert Gordon University Aberdeen United Kingdom

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

Distributed Denial of Service (DDoS) attacks are a critical threat to the security and reliability of software-Defined Networking (SDN) environments. Existing datasets for training machine learning (ML) models, such as KDDCup ’99 and CICIDS 2017, are either outdated or fail to capture SDN-specific characteristics, limiting their effectiveness in detecting modern DDoS attacks. This paper proposes a framework for generating a comprehensive, SDN-specific dataset using a virtual environment that integrates Mininet, the Ryu controller, and Python-based automation. The dataset incorporates advanced flow-level metrics, including SYN counts, queue lengths, and real-time traffic dynamics, reflecting contemporary attack scenarios such as ICMP floods, TCP SYN floods, and UDP floods. By addressing the limitations of traditional datasets, this custom dataset enhances ML model training for DDoS detection in SDN environments, providing improved accuracy and adaptability. Contributions include a scalable SDN-based dataset generation framework, enriched feature sets for ML training, and a comprehensive approach to capturing both legitimate and malicious traffic dynamics. This study highlights the potential of SDN programmability in advancing security research and offers a robust tool for the development of reliable DDoS detection mechanisms.

关键词： Training Adaptation models Accuracy Virtual environments Denial-of-service attack Vectors Robustness Real-time systems Computer crime software defined networking

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CNN-LSTM Optimized by Genetic Algorithm in Time Series Forecasting: An Automatic Method to Use Deep learning 1

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22nd international conference on Artificial Intelligence and soft computing, ICAISC 2023

作者： Urbinate, Eder Itano, Fernando Del-Moral-Hernandez, Emilio Polytechnic School University of Sao Paulo Sao Paulo Brazil

ISBN: (数字)9783031425059

ISBN: (纸本)9783031425042

Time series forecasting is a challenge in several areas and for several applications. A series of tools have emerged to tackle this problem, from classic models to modern models that use machine learning and deep learning. One of these areas of great interest is financial. Specifically, forecasting the prices of stocks and indices can be especially difficult due to the characteristics of this type of series. In this work, we propose a method to automatically develop a deep network using a genetic algorithm to select the hyperparameters. To test the proposed method, we used four datasets, including financial and non-financial time series. In both financial and non-financial datasets, the proposed method with automated hyperparameter selection did better than the models made by other authors using different methods. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Genetic algorithms

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machine learning-Based Optimization of Network Security Protocols for Enhanced Threat Detection 3

Machine Learning-Based Optimization of Network Security Prot...

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3rd IEEE international conference on Device Intelligence, computing and Communication Technologies, DICCT 2025

作者： Palani, S. Muthukumaravel, A. Bharath Institute of Higher Education and Research Department of Computer Science Tamilnadu Chennai India Bharath Institute of Higher Education and Research Faculty of Arts and Science Tamilnadu Chennai India

ISBN: (纸本)9798331543358

Current cyber threats are relatively sophisticated, and this has put pressure on traditional network security protocols. It proposes an extended machine learning methodology using a supervised Random Forest algorithm for classification and an unsupervised Autoencoder for anomaly detection to improve network security measures. The consumes network traffic through a powerful preprocessing pipeline of feature selection, scaling, and encoding to increase the performance of a model. The proposed method was also tested on benchmark datasets like CICIDS2017, UNSW-NB15, and KDDCup99 with an accuracy of 98.76%, precision of 96.45%, false positive rate under 1.89%, and a latency of only 4.23ms better than other methods from 2021, 2022 and 2023. These results show that the ability of the developed framework is higher in terms of detection and more adaptable to real-world threats. It offers a transportable and secure solution for next-generation networks, which overcomes the gaps of the previous methods to make way for further developments. © 2025 IEEE.

关键词： Support vector machines

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Comparative Study of machine learning Techniques for Intrusion Detection on CICIDS-2017 Dataset

Comparative Study of Machine Learning Techniques for Intrusi...

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international conference on computing for Sustainable Global Development (INDIACom)

作者： Shahid Arshad Wasia Ashraf Saqib Ashraf Irshad Hassan Faheem Syeed Masoodi Department of Computer Sciences University of Kashmir Kashmir India

Cyber-attacks are becoming more sophisticated and complicated, making it increasingly difficult to detect intrusions accurately. Intrusions have the potential to compromise data security, integrity, and availability, with serious consequences for individuals, organizations, and society as a whole. Several intrusion detection approaches have been presented to overcome this issue. These intrusion detection systems utilize classification algorithms to classify cyber security threats; however, due to the massive amount of data, many classification algorithms are unable to detect attacks accurately. Using feature selection/dimensional reduction approaches, data can be efficiently reduced and precisely recognized. The primary motive of this research is to examine multiple approaches used in anomaly detection based on ML (machine learning) algorithms. For every approach, we examined the performance of classifiers on CICIDS-2017 (Canadian Institute for Cybersecurity Intrusion Detection Set 2017) dataset using various feature selection/dimension reduction techniques. The data was preprocessed to remove unnecessary features whose contribution is negligible in detecting attack classes before the training or testing phase. The best algorithm for a particular attack class was finally determined by comparing accuracy of the overall model for various attack classes, including Distributed Denial of Service (DDoS), Denial of Service (DOS), Web-based, Brute force, intrusions, scans, Bots, and Heartbleed. The outcome demonstrated that, with an accuracy of 99.91%, the XGBoost classifier with Boruta feature selection outperformed the other classifiers.

关键词： Training machine learning algorithms Computational modeling Intrusion detection Organizations Feature extraction Real-time systems

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Accidental Face Recognition and Detection Using machine learning 1

Accidental Face Recognition and Detection Using Machine Lear...

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1st international conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Sharma, Ashish Agrawal, Piyansh Krishan Sharma, Bhisham Dhaou, Imed Ben GLA University Department of Computer Engineering & Applications UP Mathura India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Dar Al-Hekma University Hekma School of Engineering Computing and Informatics Department of Computer Science Jeddah Saudi Arabia

ISBN: (纸本)9781665472753

The data collected from all the States and Union Territories has been compiled in the Publication, according to the ministry of road transport and roads transport research wing. The total number of accident-related deaths in 2018 was 1,51,417, which is a 2.3 percent increase over 2017. Around 85% of accident-related deaths occur in the 18-60 age range, which is the most productive. Road traffic fatalities not only inflict the relatives of the victims considerable emotional suffering, but they also cost the nation a lot of money. In this data maximum hazard happens due to delayed response of family and friends as they are unknown of the situation of the sight of the accident. Also, several cases remain unreported. Our objective is to reduce this number to make our nation strong and prosper. In this current research we are committed to creating a social network where road accidents can be reported quickly to family and friends, so the delayed response can be reduced. The practice of associating a person with a picture has become increasingly common thanks to the media. However, it is less resistant to retinal and fingerprint scanning. The face detection and recognition module created for the current research is described in this paper. Face detection will be performed using Haar-Cascades, while face identification will be performed using Eigenfaces, Fisher faces, and local binary pattern histograms. © 2023 IEEE.

关键词： Face recognition

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